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  <h1>Source code for torch.distributions.continuous_bernoulli</h1><div class="highlight"><pre>
<span></span><span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
<span class="kn">import</span> <span class="nn">math</span>

<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch.distributions</span> <span class="kn">import</span> <span class="n">constraints</span>
<span class="kn">from</span> <span class="nn">torch.distributions.exp_family</span> <span class="kn">import</span> <span class="n">ExponentialFamily</span>
<span class="kn">from</span> <span class="nn">torch.distributions.utils</span> <span class="kn">import</span> <span class="n">broadcast_all</span><span class="p">,</span> <span class="n">probs_to_logits</span><span class="p">,</span> <span class="n">logits_to_probs</span><span class="p">,</span> <span class="n">lazy_property</span><span class="p">,</span> <span class="n">clamp_probs</span>
<span class="kn">from</span> <span class="nn">torch.nn.functional</span> <span class="kn">import</span> <span class="n">binary_cross_entropy_with_logits</span>


<div class="viewcode-block" id="ContinuousBernoulli"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli">[docs]</a><span class="k">class</span> <span class="nc">ContinuousBernoulli</span><span class="p">(</span><span class="n">ExponentialFamily</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Creates a continuous Bernoulli distribution parameterized by :attr:`probs`</span>
<span class="sd">    or :attr:`logits` (but not both).</span>

<span class="sd">    The distribution is supported in [0, 1] and parameterized by &#39;probs&#39; (in</span>
<span class="sd">    (0,1)) or &#39;logits&#39; (real-valued). Note that, unlike the Bernoulli, &#39;probs&#39;</span>
<span class="sd">    does not correspond to a probability and &#39;logits&#39; does not correspond to</span>
<span class="sd">    log-odds, but the same names are used due to the similarity with the</span>
<span class="sd">    Bernoulli. See [1] for more details.</span>

<span class="sd">    Example::</span>

<span class="sd">        &gt;&gt;&gt; m = ContinuousBernoulli(torch.tensor([0.3]))</span>
<span class="sd">        &gt;&gt;&gt; m.sample()</span>
<span class="sd">        tensor([ 0.2538])</span>

<span class="sd">    Args:</span>
<span class="sd">        probs (Number, Tensor): (0,1) valued parameters</span>
<span class="sd">        logits (Number, Tensor): real valued parameters whose sigmoid matches &#39;probs&#39;</span>

<span class="sd">    [1] The continuous Bernoulli: fixing a pervasive error in variational</span>
<span class="sd">    autoencoders, Loaiza-Ganem G and Cunningham JP, NeurIPS 2019.</span>
<span class="sd">    https://arxiv.org/abs/1907.06845</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">arg_constraints</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;probs&#39;</span><span class="p">:</span> <span class="n">constraints</span><span class="o">.</span><span class="n">unit_interval</span><span class="p">,</span>
                       <span class="s1">&#39;logits&#39;</span><span class="p">:</span> <span class="n">constraints</span><span class="o">.</span><span class="n">real</span><span class="p">}</span>
    <span class="n">support</span> <span class="o">=</span> <span class="n">constraints</span><span class="o">.</span><span class="n">unit_interval</span>
    <span class="n">_mean_carrier_measure</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">has_rsample</span> <span class="o">=</span> <span class="kc">True</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">probs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">lims</span><span class="o">=</span><span class="p">(</span><span class="mf">0.499</span><span class="p">,</span> <span class="mf">0.501</span><span class="p">),</span> <span class="n">validate_args</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="k">if</span> <span class="p">(</span><span class="n">probs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span><span class="n">logits</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Either `probs` or `logits` must be specified, but not both.&quot;</span><span class="p">)</span>
        <span class="k">if</span> <span class="n">probs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">is_scalar</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">probs</span><span class="p">,</span> <span class="n">Number</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">,</span> <span class="o">=</span> <span class="n">broadcast_all</span><span class="p">(</span><span class="n">probs</span><span class="p">)</span>
            <span class="c1"># validate &#39;probs&#39; here if necessary as it is later clamped for numerical stability</span>
            <span class="c1"># close to 0 and 1, later on; otherwise the clamped &#39;probs&#39; would always pass</span>
            <span class="k">if</span> <span class="n">validate_args</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">arg_constraints</span><span class="p">[</span><span class="s1">&#39;probs&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">check</span><span class="p">(</span><span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s1">&#39;probs&#39;</span><span class="p">))</span><span class="o">.</span><span class="n">all</span><span class="p">():</span>
                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;The parameter </span><span class="si">{}</span><span class="s2"> has invalid values&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="s1">&#39;probs&#39;</span><span class="p">))</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">probs</span> <span class="o">=</span> <span class="n">clamp_probs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">)</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">is_scalar</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">Number</span><span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="o">=</span> <span class="n">broadcast_all</span><span class="p">(</span><span class="n">logits</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_param</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</span> <span class="k">if</span> <span class="n">probs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">logits</span>
        <span class="k">if</span> <span class="n">is_scalar</span><span class="p">:</span>
            <span class="n">batch_shape</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">()</span>
        <span class="k">else</span><span class="p">:</span>
            <span class="n">batch_shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_param</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_lims</span> <span class="o">=</span> <span class="n">lims</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">ContinuousBernoulli</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">batch_shape</span><span class="p">,</span> <span class="n">validate_args</span><span class="o">=</span><span class="n">validate_args</span><span class="p">)</span>

<div class="viewcode-block" id="ContinuousBernoulli.expand"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.expand">[docs]</a>    <span class="k">def</span> <span class="nf">expand</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_shape</span><span class="p">,</span> <span class="n">_instance</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
        <span class="n">new</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_checked_instance</span><span class="p">(</span><span class="n">ContinuousBernoulli</span><span class="p">,</span> <span class="n">_instance</span><span class="p">)</span>
        <span class="n">new</span><span class="o">.</span><span class="n">_lims</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lims</span>
        <span class="n">batch_shape</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">(</span><span class="n">batch_shape</span><span class="p">)</span>
        <span class="k">if</span> <span class="s1">&#39;probs&#39;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
            <span class="n">new</span><span class="o">.</span><span class="n">probs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">batch_shape</span><span class="p">)</span>
            <span class="n">new</span><span class="o">.</span><span class="n">_param</span> <span class="o">=</span> <span class="n">new</span><span class="o">.</span><span class="n">probs</span>
        <span class="k">if</span> <span class="s1">&#39;logits&#39;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
            <span class="n">new</span><span class="o">.</span><span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">batch_shape</span><span class="p">)</span>
            <span class="n">new</span><span class="o">.</span><span class="n">_param</span> <span class="o">=</span> <span class="n">new</span><span class="o">.</span><span class="n">logits</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">ContinuousBernoulli</span><span class="p">,</span> <span class="n">new</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">batch_shape</span><span class="p">,</span> <span class="n">validate_args</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
        <span class="n">new</span><span class="o">.</span><span class="n">_validate_args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_args</span>
        <span class="k">return</span> <span class="n">new</span></div>

    <span class="k">def</span> <span class="nf">_new</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_param</span><span class="o">.</span><span class="n">new</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">_outside_unstable_region</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">le</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lims</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
                         <span class="n">torch</span><span class="o">.</span><span class="n">gt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lims</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>

    <span class="k">def</span> <span class="nf">_cut_probs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_outside_unstable_region</span><span class="p">(),</span>
                           <span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">,</span>
                           <span class="bp">self</span><span class="o">.</span><span class="n">_lims</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">))</span>

    <span class="k">def</span> <span class="nf">_cont_bern_log_norm</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="sd">&#39;&#39;&#39;computes the log normalizing constant as a function of the &#39;probs&#39; parameter&#39;&#39;&#39;</span>
        <span class="n">cut_probs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cut_probs</span><span class="p">()</span>
        <span class="n">cut_probs_below_half</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">le</span><span class="p">(</span><span class="n">cut_probs</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">),</span>
                                           <span class="n">cut_probs</span><span class="p">,</span>
                                           <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">cut_probs</span><span class="p">))</span>
        <span class="n">cut_probs_above_half</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ge</span><span class="p">(</span><span class="n">cut_probs</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">),</span>
                                           <span class="n">cut_probs</span><span class="p">,</span>
                                           <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">cut_probs</span><span class="p">))</span>
        <span class="n">log_norm</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">log1p</span><span class="p">(</span><span class="o">-</span><span class="n">cut_probs</span><span class="p">)</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">cut_probs</span><span class="p">)))</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">le</span><span class="p">(</span><span class="n">cut_probs</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">),</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">log1p</span><span class="p">(</span><span class="o">-</span><span class="mf">2.0</span> <span class="o">*</span> <span class="n">cut_probs_below_half</span><span class="p">),</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mf">2.0</span> <span class="o">*</span> <span class="n">cut_probs_above_half</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
        <span class="n">taylor</span> <span class="o">=</span> <span class="n">math</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mf">2.0</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="mf">4.0</span> <span class="o">/</span> <span class="mf">3.0</span> <span class="o">+</span> <span class="mf">104.0</span> <span class="o">/</span> <span class="mf">45.0</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">x</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_outside_unstable_region</span><span class="p">(),</span> <span class="n">log_norm</span><span class="p">,</span> <span class="n">taylor</span><span class="p">)</span>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">mean</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">cut_probs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cut_probs</span><span class="p">()</span>
        <span class="n">mus</span> <span class="o">=</span> <span class="n">cut_probs</span> <span class="o">/</span> <span class="p">(</span><span class="mf">2.0</span> <span class="o">*</span> <span class="n">cut_probs</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">log1p</span><span class="p">(</span><span class="o">-</span><span class="n">cut_probs</span><span class="p">)</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">cut_probs</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</span> <span class="o">-</span> <span class="mf">0.5</span>
        <span class="n">taylor</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">+</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="mf">3.0</span> <span class="o">+</span> <span class="mf">16.0</span> <span class="o">/</span> <span class="mf">45.0</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span> <span class="o">*</span> <span class="n">x</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_outside_unstable_region</span><span class="p">(),</span> <span class="n">mus</span><span class="p">,</span> <span class="n">taylor</span><span class="p">)</span>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">stddev</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">variance</span><span class="p">)</span>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">variance</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">cut_probs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cut_probs</span><span class="p">()</span>
        <span class="nb">vars</span> <span class="o">=</span> <span class="n">cut_probs</span> <span class="o">*</span> <span class="p">(</span><span class="n">cut_probs</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="mf">2.0</span> <span class="o">*</span> <span class="n">cut_probs</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">log1p</span><span class="p">(</span><span class="o">-</span><span class="n">cut_probs</span><span class="p">)</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">cut_probs</span><span class="p">),</span> <span class="mi">2</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
        <span class="n">taylor</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="mf">12.0</span> <span class="o">-</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="mf">15.0</span> <span class="o">-</span> <span class="mf">128.</span> <span class="o">/</span> <span class="mf">945.0</span> <span class="o">*</span> <span class="n">x</span><span class="p">)</span> <span class="o">*</span> <span class="n">x</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_outside_unstable_region</span><span class="p">(),</span> <span class="nb">vars</span><span class="p">,</span> <span class="n">taylor</span><span class="p">)</span>

<div class="viewcode-block" id="ContinuousBernoulli.logits"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.logits">[docs]</a>    <span class="nd">@lazy_property</span>
    <span class="k">def</span> <span class="nf">logits</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">probs_to_logits</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">,</span> <span class="n">is_binary</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>

<div class="viewcode-block" id="ContinuousBernoulli.probs"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.probs">[docs]</a>    <span class="nd">@lazy_property</span>
    <span class="k">def</span> <span class="nf">probs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">clamp_probs</span><span class="p">(</span><span class="n">logits_to_probs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="n">is_binary</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span></div>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">param_shape</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_param</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>

<div class="viewcode-block" id="ContinuousBernoulli.sample"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.sample">[docs]</a>    <span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">()):</span>
        <span class="n">shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_extended_shape</span><span class="p">(</span><span class="n">sample_shape</span><span class="p">)</span>
        <span class="n">u</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">icdf</span><span class="p">(</span><span class="n">u</span><span class="p">)</span></div>

<div class="viewcode-block" id="ContinuousBernoulli.rsample"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.rsample">[docs]</a>    <span class="k">def</span> <span class="nf">rsample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_shape</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">()):</span>
        <span class="n">shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_extended_shape</span><span class="p">(</span><span class="n">sample_shape</span><span class="p">)</span>
        <span class="n">u</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">icdf</span><span class="p">(</span><span class="n">u</span><span class="p">)</span></div>

<div class="viewcode-block" id="ContinuousBernoulli.log_prob"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.log_prob">[docs]</a>    <span class="k">def</span> <span class="nf">log_prob</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_args</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_sample</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
        <span class="n">logits</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">broadcast_all</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
        <span class="k">return</span> <span class="o">-</span><span class="n">binary_cross_entropy_with_logits</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s1">&#39;none&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cont_bern_log_norm</span><span class="p">()</span></div>

<div class="viewcode-block" id="ContinuousBernoulli.cdf"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.cdf">[docs]</a>    <span class="k">def</span> <span class="nf">cdf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_args</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_sample</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
        <span class="n">cut_probs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cut_probs</span><span class="p">()</span>
        <span class="n">cdfs</span> <span class="o">=</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">cut_probs</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">cut_probs</span><span class="p">,</span> <span class="mf">1.0</span> <span class="o">-</span> <span class="n">value</span><span class="p">)</span>
                <span class="o">+</span> <span class="n">cut_probs</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="mf">2.0</span> <span class="o">*</span> <span class="n">cut_probs</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span>
        <span class="n">unbounded_cdfs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_outside_unstable_region</span><span class="p">(),</span> <span class="n">cdfs</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">le</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">),</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">value</span><span class="p">),</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ge</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">value</span><span class="p">),</span> <span class="n">unbounded_cdfs</span><span class="p">))</span></div>

<div class="viewcode-block" id="ContinuousBernoulli.icdf"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.icdf">[docs]</a>    <span class="k">def</span> <span class="nf">icdf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_args</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_validate_sample</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
        <span class="n">cut_probs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cut_probs</span><span class="p">()</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_outside_unstable_region</span><span class="p">(),</span>
            <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">log1p</span><span class="p">(</span><span class="o">-</span><span class="n">cut_probs</span> <span class="o">+</span> <span class="n">value</span> <span class="o">*</span> <span class="p">(</span><span class="mf">2.0</span> <span class="o">*</span> <span class="n">cut_probs</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">))</span>
             <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">log1p</span><span class="p">(</span><span class="o">-</span><span class="n">cut_probs</span><span class="p">))</span> <span class="o">/</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">cut_probs</span><span class="p">)</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">log1p</span><span class="p">(</span><span class="o">-</span><span class="n">cut_probs</span><span class="p">)),</span>
            <span class="n">value</span><span class="p">)</span></div>

<div class="viewcode-block" id="ContinuousBernoulli.entropy"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.continuous_bernoulli.ContinuousBernoulli.entropy">[docs]</a>    <span class="k">def</span> <span class="nf">entropy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="n">log_probs0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">log1p</span><span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">)</span>
        <span class="n">log_probs1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">mean</span> <span class="o">*</span> <span class="p">(</span><span class="n">log_probs0</span> <span class="o">-</span> <span class="n">log_probs1</span><span class="p">)</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cont_bern_log_norm</span><span class="p">()</span> <span class="o">-</span> <span class="n">log_probs0</span></div>

    <span class="nd">@property</span>
    <span class="k">def</span> <span class="nf">_natural_params</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="p">)</span>

    <span class="k">def</span> <span class="nf">_log_normalizer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;computes the log normalizing constant as a function of the natural parameter&quot;&quot;&quot;</span>
        <span class="n">out_unst_reg</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">le</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lims</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">),</span>
                                 <span class="n">torch</span><span class="o">.</span><span class="n">gt</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lims</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">))</span>
        <span class="n">cut_nat_params</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">out_unst_reg</span><span class="p">,</span>
                                     <span class="n">x</span><span class="p">,</span>
                                     <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_lims</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
        <span class="n">log_norm</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">cut_nat_params</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">))</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">cut_nat_params</span><span class="p">))</span>
        <span class="n">taylor</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="n">x</span> <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="mf">24.0</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span> <span class="o">/</span> <span class="mf">2880.0</span>
        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">out_unst_reg</span><span class="p">,</span> <span class="n">log_norm</span><span class="p">,</span> <span class="n">taylor</span><span class="p">)</span></div>
</pre></div>

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